Finding non-Gaussian components of high-dimensional data is an important preprocessing step for efficient information processing. This article proposes a new linear method to identify the "non-Gaussian subspace" within a very general semi-parametric framework. Our proposed method, called NGCA...

Finding non-Gaussian components of high-dimensional data is an important preprocessing step for efficient information processing. This article proposes a new linear method to identify the non-Gaussian subspace within a very general semi-parametric framework. Our proposed method, called NGCA...

This paper presents two new tools for the identification of faking interviewers in surveys. One method is based on Benford?s Law, and the other exploits the empirical observation that fakers most often produce answers with less variability than could be expected from the whole survey. We focus...

This paper presents two new tools for the identification of faking interviewers in surveys. One method is based on Benford's Law, and the other exploits the empirical observation that fakers most often produce answers with less variability than could be expected from the whole survey. We focus...

Summary A common assumption in supervised learning is that the training and test input points follow the same probability distribution. However, this assumption is not fulfilled, e.g., in interpolation, extrapolation, active learning, or classification with imbalanced data. The violation of this...